ABSTRACT The goal of this Phase I SBIR is to address the absence of measureable and targeted metabolite biomarkers in clear cell renal cell carcinoma (ccRCC) tissues that can predict disease metastasis. An estimated 69,990 Americans were diagnosed with kidney cancer in 2017, and approximately 14,400 died as a result of ccRCC, primarily due to metastasis of the cancer. Ultimately, we seek to identify those patients with more aggressive renal cancers that might benefit from more aggressive treatment. Patients with indolent ccRCC may choose less aggressive treatment while those with a greater risk of metastasis may elect adjuvant therapy that may include drug treatments. There are no diagnostic tests that predict post-surgical disease progression on the market. Our methodology will utilize novel liquid chromatography (LC) and mass spectrometry (MS) methods developed by our collaborators, along with several new methods we developed that will greatly increase quantitative accuracy and robustness. This will address a series of issues that have plagued the development of a reliable biomarker panel for the diagnosis of renal cancer progression and metastasis. A major problem with metabolomic studies to date is that the heterogeneity of cancer tissues is frequently overlooked. Thus, an important part of our approach includes normalization, calibration, and quantitation of metabolites in both discovery and targeted modes. Specifically, our method allows for the analysis of metabolites in small biopsies and will permit histopathology to be performed on exactly the same tissue. Whereas normalization and quantitation are usually addressed after data has been acquired, i.e. post- acquisition, we add pre-acquisition normalization, which will ensure that equal amounts of sample are injected for MS. Our use of chemical labeling and sample normalization will minimize the effects of ion suppression, signal saturation, column contamination, aging, and instrument performance drift. Essentially, the day-to-day and lab-to-lab variability, which frequently affect MS, will be minimized. In this proposal, we will utilize the Eastern Virginia Medical School Biorepository PROBE cohort, which houses over 300 renal tumors and associated clinical data, including treatment, vital status, imaging data, and longitudinal follow-up after primary tumor resection. The database accompanying the Biorepository is regularly updated, allowing us to identify those who ?progressed? vs. those who are termed ?non-progressors?. For this Phase I proof-of-concept study, tissues from 26 patients that developed metastasis will be analyzed and compared to 174 patients that did not develop metastasis (n=200 patients). All tumor tissues ? primary and metastatic ? will be paired with ?normal? tumor-adjacent tissue from clear margins. In addition, larger numbers of patients in the cohort will be analyzed using available histopathological and staging data as surrogates for clinical outcome. A side benefit to this study is the prospective addition to our ccRCC bank for Phase II studies.